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Jordan Wilson (0:46)

A few weeks ago, the United States government said the quiet part out loud when it comes to open source source models, at least from China. That's because in April, the White House sent out an official memo accusing China of using distillation to illegally copy American AI models to create cheaper domestic knockoffs. And that declaration is really nothing new. If you've followed AI for years. However, the recent distillation trend has completely reshaped one important landscape of enterprise AI the decision between using open source models versus proprietary closed models. And in 2026 at least, it can actually be a tough choice between saving potentially millions of dollars versus running up your legal liability. About two years ago, before Chinese distillation was commonplace, there was a sizable gap between frontier models and open source AI models or the those models that you can essentially download or use for close to free. But now the gap is all but closed, which has thrust the open source versus closed source question into every enterprise boardroom in 2026. And although there's no one size fits all answer, we're going to be tackling the toughest topics and the most important takeaways as we take a zoomed out view of open source models on today's show. That's why we're going over open source AI 101, why local models, cheap APIs and AI agents change everything about making AI decisions in 2026 as part of our Start Here series. All right, welcome to Everyday AI. Before we dig in, let's first zoom out and talk about the big picture here when it comes to open source AI. That's because, well, it's actually a legitimate thing now, right? Two years ago, enterprise companies weren't saying let's use an open source AI model in production. Today it's actually happened. It's happening. That's because, you know, maybe the most powerful open source models are only about two to six months behind frontier models, but on even consumer hardware you can be running essentially frontier level AI models from like just over 12 months ago. And the Chinese labs now distilling US models have kind of crashed the open API prices to pennies. Right? So yeah, not everyone out there on, you know, consumer or prosumer hardware can run the most powerful open source models, although I do think Google has something to say about that. But the most powerful open source models run for a fraction of the actual cost if you can't afford to run them locally, which has completely shifted the paradigm when it comes to enterprises making decisions on well, are we going to use a model from one of the big three OpenAI, Google or Anthropic, or are we going to use a Chinese open source model and pay for it that way? And well, what this has also led to in 2026 is essentially 24, seven local agents that can run and also without costing a ton, and now having to actively and almost aggressively aggressively go through kind of an AI cost triage. But going full open source does strip away the legal protection that the closed models often include. So stick with me for 25ish minutes on today's Start Here series show and here's what you're going to learn. You're going to know why the open versus closed source AI default just officially flipped. You're going to know how Gemma 4 from Google puts year old Frontier capability on your laptop. You're going to understand the two payoffs already shaping and reshaping how individuals and enterprises run AI and the hidden legal trade off most executives miss when going fully open source. Let's get into it. My name is Jordan Wilson. Welcome to Everyday AI's Start Here series. This is the essential podcast series to learn the AI basics. And if you're an and AI expert, this is your chance to freshen up and double down on your AI knowledge. Why do we start this Start Here series? Well, after 750 plus podcasts, I never really had a good answer when someone was like, where do I start? What podcast do I start with? That's why we created the Start Here series. It's best, I think if you listen in order. I think this is now volume 24 of the start Here series. So maybe we'll wrap it up at 25, maybe we'll wrap it up at 30. I'm not sure. But the whole point of this is you can go to start hereseries.com that's going to give you free access to our exclusive inner circle community. And in the Start Here series space we make it even easier for you so you can actually go listen. We have a Spotify playlist ready for all of the different Start Here Series shows as well as a breakdown on each Individual episode all in one place. So make sure you go to start here series.com for exclusive access to that inside of our inner circle community. All right? And if you missed our last Start Here series Show, that was volume 23. We talked about headless software and why companies are building software for AI agents and not humans and well, what that means. So today in volume 24 of the start Here series, we're going over open source AI101. So here's the reality. Closed AI used to be the de facto, right? And I, I mean honestly there was really never even much of a discussion about open source AI in the enterprise maybe until 2025 at least. Not serious enterprise companies. Now it's a, it's a real conversation, right? So it's no longer, you know, hey, we're just going to choose whichever API works best for us, right? Whether that's OpenAI, anthropic or Google. Now most companies are looking at some of the open source alternatives, most of them could coming from China. And the big kind of thing here is companies are starting to standardize around one frontier vendor in 2025, before this happened. And then they called it an AI strategy. And the assumption originally was, well, that worked for three years until two very specific forces broke the standard paradigm when it came to open source models. First, the proprietary versus closed gap capabilities just completely changed. All right, so we talk about arena on here a lot for previously had a different name, now it's just Arena. So you put in a prompt, you don't know the outputs, you know what models they're from and you vote for which one is better, right? So all these different models get an ELO score and to really zoom out for our non technical audience, because I know a lot of you in the Start Here series are not technical, I'd probably even say what an open source model even is, right? So the very simplified version is certain companies can release models open source under like an MIT or Apache 2.0 license. And that gives people the ability to download these actual models and to run them locally on your machine. And that is, well one of the big trade offs, right? So you're not sending any private or potentially proprietary data in the cloud at all. Everything runs locally on your machine. So number one, it's private, number two, it's well, free, right? And then there are open source models that if you can't download them on your, you know, computer, because not everyone can, some of them are much larger, you can still essentially run those in the cloud for a fraction of the Cost of what it would cost to run a proprietary model. So essentially, open source models are ones that you can download, you can modify. In some instances, you can even build products on top of it. All right, Anyways, right, until late 2025, there was a monstrous gap in the arena scores, right? So these ELO scores, when you put in the same prompt, you look at two outputs. Everyone overwhelmingly always chose the best front, the best frontier closed source model. And that really started to change, right? So the gap between the ELO scores, well, it cut down by about 90%. So it went from about a 250 point gap from the best frontier or closed source model. Well, now it's only about 30 points, right? Give or take, depending on the day. Right. But it's even, you know, recently, a couple months ago, it was like 15 points, right? So at that point, you really have to be an AI expert to be able to decipher the difference. I think 30 points, you know, most people could look at different outputs over time and you know, a 30 point, you can kind of realize it. If you're looking at the best, you know, open source model versus the best proprietary closed models, 30 points you can understand. But 10 to 15 points, it's kind of a coin flip even for people who are, you know, spending most of their days inside of large language models. But this collapse came from those two different forces, one working in parallel. So I have a little, little graphic here on the screen for our live stream audience. If you are listening on the podcast, FYI, you can always get the video version on our website at your everyday AI.com but going from a 250 point gap to essentially a 30 point gap, this is huge, right? Because like I said, in 2023 to mid-2025, it was noticeable, right? It was extremely. When you looked at the outputs, you could say, my business can use output A, but it cannot use output B. Right? And now we're at the point where the open source models, in terms of an ELO score, and I think that's a good metric to look at over time, right? Because you know, the frontier is always improving. But if you look at the ELO scores of the open source models now, so those that you can kind of, you know, if you have a beefy enough computer, you can download some of the best open source models on your actual local machine, right? Those scores are where we were at with proprietary models three to six months ago, right? So think back to the very end of 2025, you know, and there's some models like I Think at the time was probably GPT5 3, Gemini 3 Pro. And at that time I think we were at like opus, maybe 4, 6 or maybe 4, 5. Right now you have open source models that you can run for free 24, 7, run agentically that are at that same level. And that's why now this is a real enterprise boardroom problem, especially for large companies that have invested in heavily into AI, right? So I'm not talking about companies that, you know, with a couple hundred employees. I'm talking about companies that were spending seven, maybe eight, maybe even more seven to eight figures on AI each year. Now all of a sudden they're saying hey, in theory, if we switched, you know, part of our, you know, summarization tasks alone, right? There's, I, I've read a lot, I've, I've talked with a lot of people that have done something similar. You know, if, if, if we just, you know, chunk off everything that we're using, you know, open source model or sorry, closed source model just for summarizing text, right? Some of those lower hanging fruit people are saying well yeah, we could save 1, 2, 3, $4 million. And this is an actual reality that a lot of companies are grappling with right now. So the force one was just the capability moving locally, right? And this, I think we have to credit Google for pushing the edge of Edge AI. That's because with their Gemma 4 model completely shook up the landscape of open source AI, right? This thing was 20 times more efficient than other open source AI models at the time. So essentially, let me describe it like this. Do you remember GPT4O? Right. One of the best models, you know, about 14, 15 months ago, it was at the absolute frontier, right? So now you can download Gemma 4 on a consumer laptop and it has, you know, roughly you look at the scientific benchmarks and the Elo score, it's essentially a, about a GPT4O level model that you can run on your laptop. So what's the big deal? What's the big difference, right? 14, 15 months ago, I mean there's thousands of companies spending millions of dollars a year to get that type of technology, to get GPT4O level technology for their employees. Now doesn't take, take anything really. It takes a newish piece of Apple, right? I, I just got a, a new MacBook Pro. That thing can run Gemma 4 very easily, right? It can run even better models than that. And, and this really changes, I think what is ultimately capable when you look at the open source versus closed source. Because yes, I think most people look at normal usage and they're comparing apples to apples, right? Here's what our marketing team did 15 months ago with a GPT4O level model. Oh, now they can do that on Gemma 4? Well, yeah, you can do it, but now you can do it agentically because not only in the last year or so have the models obviously improved with now thinking models, reasoning models being the default, but now we have these agentic harnesses, you know, not just the ones that you can use, you know, inside of Chat, gbt, Gemini, Claude Copilot. But, well, you have these local autonomous AI systems as well, such as OpenClaw, such as I always forget if it's Hermes or Hermes Agent, right? So now you can have essentially the level of AI from 14 months ago running for free 24,7 agentically, even if you're just doing it, you know, summarization, content creation, research, things like that. So when capabilities went local, right? Gemma Ford leading the way. But obviously all the Chinese models followed suit because of right distillation, which we'll talk about a little bit here in a couple of minutes. But you can't overlook Gemma because it put frontier capability on literally a laptop, right? Because two years ago to be able to run something like a GPT4O level model, right, which rumors have been swirling that it's a 2 trillion parameter model, right, you would need a small little data center to run something like that, you know, 2ish years ago. Now you have these capabilities. So you know, I been lucky to talk to a lot of smart people in AI and now you really have executives grappling with, well, should we be buying a bunch of, as an example, new MacBooks? Should we be, you know, buying a bunch of DGX Sparks for our employees and setting them up with 247 always on Agentic AI, right, to take advantage of these now local and powerful models that, well, you don't pay, right? You download them once, you don't pay again and they work and they can work while you sleep. Like I said, because of some of the new autonomous capabilities from local agents that can run around the clock. So this has obviously led to, and I think Google putting the pressure on the, the open source world with Gemma 4. Like I said, it was 20 times more efficient in terms of what is it was able to achieve on the benchmarks in terms of size, right? Because when it came out, if you looked at the other Chinese models, it was about 10 to 20 times smaller in size, right? So you wouldn't have been able to, you know, use the best open Source Model Pre Gemma 4 on a local machine, right? At least a consumer laptop that you can just go walk into the store and buy now you can. And open. Chinese models are amazing and I think they've been getting better and better and smaller and smaller and more and more efficient since Google's Gemma 4. But you have to talk about the elephant in the room that is these models are distilled, right? We can say that. All the big labs have said that, you know, are, I don't know, maybe some of our audience in China won't appreciate hearing that. But I Mean, Google, Anthropic, OpenAI have all accused China and have said they have proof, right? But the, the White House. So in April, the White House actually officially said that China was using kind of illegal tactics to distill USAI models to create cheaper domestic knockoffs, right? So it got to the point that at least the White House said that they had enough information or intel to make that declaration. So what is model distillation? And well, why does it matter? So the easiest way is like I can spend 10 hours studying for a test, right? Think back in the classroom, I can spend 10 hours sitting for a test. Someone behind me can look over my shoulder and spend 10 minutes and get the exact same answers. That's kind of like what model distillation is, right? You have the big AI companies here in the US spending billions of dollars, right, on any single new, you know, model pre training as an example. And essentially you have certain actors in China who will use the API. And you know, different companies have come out with different levels of proof and say, okay, well they're creating, you know, thousands of spoof accounts, more or less. They're putting in all these inputs and training it on our model's outputs versus training it themselves. So yeah, just kind of copying the homework. So what this has led to is China has been able to put out these open source models really technically just pushing the frontier of open source by allegedly just copying the best US models out there. And what this has led to as well, it's a crashing out at the bottom price of intelligence. So Deep Seek v4 as an example, Deep Seq, one of those companies that many of the AI labs here in the US have accused of model distillation. Deepseek V4 Pro, one of their newer models, now list their price at 43 cents per million token inputs and 87 cents per million token outputs. That's like more than 25 times cheaper than the premium, you know, Closed source proprietary models. And that is the reality that a lot of board rooms are looking at right now. Right. To make that math easy it's like okay, it, it, we're spending $1,000 per month per employee on the API side, right. If you're, or sorry, if you're spending, let's just say $40,000 a year. Okay, we could be spending $1,000 a year if we switch over to an open source model as an example. So that's, here's what that actual leads that what that actually leads to, right. Kind of the, the model distillation leads to more powerful, cheaper open source models from China and well, it leads to people using them but not always knowing the ramifications. Right. So obviously Google doing things the right way. But I think with these Chinese models they've become increasingly popular even in the enterprise, which is tricky and I don't think that most executives are fully understanding some of the consequences of using open source models. But this has led to essentially having a workforce of always on assistance and they've shift from expensive special projects to well, that's just now the default operating model. So you know, this has just allowed kind of these, this new swarm of agentic AI that couldn't have really have existed before. Number one, the technology and the harnessing wasn't there. But number two, you take out, you know, at least Gemma and the Chinese models that have been accused of distillation and your, your options, aside from those aren't really that good. All right, we're going to talk more here after we take a quick break for a word from our partners.